Fluorescence Polarization‐Based Measurement of Protein‐Ligand Interaction in Fungal Cell Lysates
Bibliographic record
Abstract
Fungi infect over a billion people worldwide and contribute substantially to human morbidity and mortality despite all available therapies. New antifungal drugs are urgently needed. Decades of study have revealed numerous protein targets of potential therapeutic interest for which potent, fungal-selective ligands remain to be discovered and developed. To measure the binding of diverse small molecule ligands to their larger protein targets, fluorescence polarization (FP) can provide a robust, inexpensive approach. The protocols in this article provide detailed guidance for developing FP-based assays capable of measuring binding affinity in whole cell lysates without the need for purification of the target protein. Applications include screening of libraries to identify novel ligands and the definition of structure-activity relationships to aid development of compounds with improved target affinity and fungal selectivity. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Use of saturation binding curves to optimize tracer and lysate protein concentrations Basic Protocol 2: Establishment of competition binding experiments Support Protocol 1: Preparation of fungal cell lysates Support Protocol 2: Preparation of human HepG2 cell lysate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".